Electroencephalography (EEG) is a well-established method for assessing brain activity by recording and analyzing the weak biopotential signals generated in the cortex of the brain with electrodes attached on the skin of the skull surface. The EEG has been in wide use for decades in basic research of the neural systems of the brain, as well as in clinical diagnosis of various neurophysiological diseases and disorders. Since the present invention is based on an EEG signal, the EEG signal and its appearances in systemic disorders are discussed first.
The EEG signal represents the sum of excitatory and inhibitory potentials of large numbers of cortical pyramidal neurons, which are organized in columns. Each EEG electrode senses the average activity of several thousands of cortical pyramidal neurons.
The EEG signal is often divided into four different frequency bands: Delta (0.5–3.5 Hz), Theta (3.5–7.0 Hz), Alpha (7.0–13.0 Hz), and Beta (13.0–32.0 Hz). Alpha waves are found during periods of wakefulness, and they may disappear entirely during sleep. Beta waves are recorded during periods of intense activation of the central nervous system. The lower frequency Theta and Delta waves reflect drowsiness and periods of deep sleep.
Different derangements of internal system homeostasis disturb the environment in which the brain operates, and therefore the function of the brain and the resulting EEG are disturbed. The EEG signal is a very sensitive measure of these neuronal derangements, which might be reflected in the EEG signal either as changes in membrane potentials or as changes in synaptic transmission. A change in synaptic transmission occurs whenever there is an imbalance between consumption and supply of energy in the brain. This means that the EEG signal serves as an early warning of a developing injury in the brain.
Diagnostically, the EEG is not specific, since many systemic disorders of the brain produce similar EEG manifestations. In the ICUs, an EEG signal may be of critical value, as it may differentiate between broad categories of psychogenic, epileptic, metabolic-toxic, encephalitic and focal conditions. However, in many of these conditions, the EEG signal is incidental and hardly critical to the diagnosis or clinical management. However, the EEG signal may be used to detect intracranial changes earlier than by conventional methods. According to the present state of knowledge, the EEG signal is regarded as an effective tool for monitoring changes in the cerebral state of a patient.
In ICU surroundings, the EEG signal is quite often measured by using two channels: C3-P3 and C4-P4. In this way, localized abnormalities are overlooked, but the measurement is easier to carry on, since the number of electrodes is small. Long-term monitoring of the EEG is not yet common in ICU surroundings, and so far the emphasis has been on diagnostic applications using the raw EEG signal.
Encephalopathy commonly refers to metabolic and systemic disorders of the brain, which are secondary to and/or consequent upon systemic diseases or conditions. Encephalopathies are not cerebral in origin.
Recent studies of non-sedated, critically ill patients suggest that up to 70 per cent of such patients have clinical evidence of altered consciousness consistent with encephalopathy. The precise effect of critical illness encephalopathy, i.e. a distinct form of encephalopathy, is not known, as there is no agreed way of diagnosing it.
Most of the metabolic and systemic disorders have EEG correlates, and if there is a disturbance of conscious level, the EEG is never normal. However, the EEG findings in encephalopathy have many similarities to those during sedation and anesthesia, which makes the detection of encephalopathy in sedated patients difficult. Generally, when a patient loses consciousness, a shift of spectral power towards lower frequencies appears. In a sedated but healthy brain, sedation induces more order in the EEG signal. Sedation is often said to resemble natural sleep, even though counter arguing opinions exist. An EEG signal measured during sleep is said to have a structural polyphasic pattern. This pattern should be quite different from the one seen in most metabolic disorders, which in its more severe manifestations appears as a monotonic shift towards statistical stationarity.
The difficulty of distinguishing encephalopathy from sedation may be one reason for the EEG being often an underused and a misunderstood clinical resource. Namely, it is often so that as the brain is buffered by the blood-brain barrier, the EEG correlates with clinical behavioural states are better than with peripheral chemistry, such as blood samples. These correlations are enhanced by the objectivity of quantitative EEG (qEEG) measures, even though no consensus of the appropriate methodology of qEEG measurements exists at the moment. A lack of international recommendations has decelerated the advances in commercially available algorithms and monitoring platforms. Below, different uses of the EEG in an ICU are discussed.
Briefly, the EEG signal has a limited repertoire of responses in most of the systemic metabolic disorders, which may be of help when detecting encephalopathies. However, none of the responses is in any way specific to particular conditions. The following describes the repertoire of EEG responses during encephalopathy:                slowing of the dominant alpha rhythm, where a frequency of 7.5 Hz can be taken as abnormal;        an increase in Theta frequencies;        often paroxysmal Delta activity;        triphasic waves;        fallout of all faster frequencies and a falling amplitude (terminal patients); and        isoelectricity (terminal patients).        
The monitoring and detection of the above conditions is based on the clinician's subjective analysis of the raw EEG signal. In the following, the current monitoring methods of some encephalopathy types are discussed briefly.
In status epilepticus the epileptic spikes typically last only for a fraction of seconds, but the use of the EEG leans towards the fact that by using long lasting recordings, the EEG signal can reflect slow trend changes. Also, if a seizure occurs during measurements, the EEG signal may categorize the epileptiform patterns and seizure activity as a specific type of epilepsy, as well as identify the non-convulsive forms of status epilepticus. In addition, the EEG signal may be used as a control tool for inducing a barbiturate sleep to a level where there are no visible seizures. In this kind of monitoring, an EEG signal from several channels is needed.
In coma, which is the far end of a metabolic encephalopathy, or if brain death is suspected, EEG correlates with the grade of neuronal dysfunction (severity of brain injury) and therefore gives a prognosis for the recovery of the patient. In this sense, the clinician again interprets the raw EEG. A good sign concerning the patient's recovery is the reactivity of the EEG signal. On the other end, in a deeper coma, the isoelectricity of EEG suggests brain death. Despite this important indication, the isoelectricity of the EEG signal does not alone confirm the brain death; other methods like angiography are used as well to ensure the diagnosis.
Global cerebral hypoxia and ischemia belong to the commonest of all clinical disorders of the brain, and they result from local or generalized hypoxic-ischemic events. The patterns of the EEG signal include, for example, generalized suppression (isoelectric EEG), which after a day of recording indicates either permanent vegetative state or death, and burst suppression, which in turn predicts a poor outcome after a head trauma, for example. Although being categorized as a primary brain injury, generalized cerebral hypoxia is also a true metabolic disorder, and can be categorized as encephalopathy.
Generally, if a patient is unconscious (without sedation), the reason in 30 to 40 per cent of the cases is intracranial, whereas in 60 to 70 per cent of the cases unconsciousness is due to hypoxic-ischaemic, metabolic, or toxic reasons. This kind of general unconsciousness is currently monitored with the help of the Glasgow Coma Scale. It defines the patient (un)consciousness by using three parameters: eye opening, best motoric response, and best response to speech. Table 1 below illustrates the Glasgow Coma Scale. As can be seen, this method is subjective and inter-rater variability may be large.
TABLE 1The Glasgow Coma ScaleCriterionPointsEye Opening ResponseSpontaneous4Opens to verbal command3Opens to pain2None1Verbal responseOriented5Confused4Inappropriate words3Incomprehensible sounds2None1Motor responseObeys commands6Localises pain5Withdraws from pain4Abnormal flexion3Extends to pain2None1Total3–15
Virtually every patient being cared for in an ICU receives some form of sedation. However, the control of the depth of the sedation administered to a patient is still problematic, and therefore oversedation and undersedation are both common occurrences in ICUs. Although sedation assessment is currently evolving towards a more disciplined and standard part of clinical practice, in which different objective sedation assessment tools are used in order to improve the reliability of the sedation assessment, monitoring the level of sedation is at present mainly handled by using subjective observations from the patient. Various sedation assessment scales have been developed for subjectively assessing the level of sedation, the Ramsay Score being one of the most widely used tool for this purpose. These scoring systems typically assess the different components of the state of the patient, namely motoric and hypnotic components, and the level of agitation.
An objective tool for assessing the level of anesthesia or sedation is disclosed in international patent application WO 02/32305, which depicts a method and device for ascertaining the cerebral state of a patient. In this disclosure, a measure derived from EMG signal data enhances and confirms the determination of the hypnotic state made using EEG signal data. As the EMG data may be computed more frequently than the EEG data, this renders ascertaining changes in the hypnotic state of the patient more rapid. The combined indication provided by the EEG signal data, indicative of the hypnotic component, and EMG signal data, indicative of the motoric component, may also be used for assessing the adequacy of anesthesia or the level of sedation.
However, the new tools for assessing anesthesia and sedation are not able to detect encephalopathy, and no specific parameters for the automatic detection of encephalopathy exist at the moment. Therefore, the detection is at present still based on a clinician's subjective analysis on the state of the patient and the characteristics of the EEG signal. However, the detection of encephalopathy is difficult, since there is often a contradiction between the external appearance and the actual state of the patient, especially in an ICU where the patients are normally sedated. A patient having encephalopathy may appear as being awake in a sort of ailing state. As a result of this, the infusion rates of sedatives may be increased, although the actual state of the patient would not require such an increase. This increases the risk of over-sedation and the problems related to that.
The present invention seeks to eliminate the above-mentioned drawbacks and to bring about a method by means of which encephalopathic patients may be distinguished from patients being sedated but having a healthy brain.